Abstract: Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the ''local'' context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the ''global'' context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity in BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates ''global'' and ''local'' perspectives into the BIQA methodology. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the \textbf{global context} of the dataset. 2) A Quality-aware mask attention module, which employs the random mask to ensure the consistency with visual \textbf{local sensitivity}, thereby improving the model's perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods, \emph{i.e.,} achieving the PLCC values of 0.941 (\textcolor{red}{$\uparrow 3.3\%$} vs. 0.908 in TID2013) and 0.920 (\textcolor{red}{$\uparrow 2.6\%$} vs. 0.894 in LIVEC).